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A machine learning approach to determine the risk factors for fall in multiple sclerosis.

Suriye ÖgürMeryem Koçaslan Toranİsmail ToygarGizem Yağmur YalçınMefküre Eraksoy
Published in: BMC medical informatics and decision making (2024)
In this study, smoking and regular exercise were the modifiable factors contributing to falls in PwMS. We recommend that clinicians facilitate the modification of these factors in PwMS. Age and disease duration were non-modifiable factors. These should be considered as risk increasing factors and used to identify PwMS at risk. Interventions aimed at reducing MSIS-29 and EDSS scores will help to prevent falls in PwMS. Education of individuals to increase knowledge and awareness is recommended. Financial support policies for those with low income will help to reduce the risk of falls.
Keyphrases
  • multiple sclerosis
  • machine learning
  • healthcare
  • physical activity
  • public health
  • quality improvement
  • resistance training